1. Field of the Invention
The invention relates generally to computer simulations of biological networks.
2. Background Information
In the past few decades the rapid gain of information about intracellular signal transduction and genetic networks has led to the view of regulatory biomolecular circuits as highly structured multi-component systems that have evolved to perform optimally in very uncertain environments. This emergent complexity of biochemical regulation necessitates the development of new tools for analysis, most notably computer assisted mathematical models. Computer modeling has proved to be of crucial importance in the analysis of genomic DNA sequences and molecular dynamics simulations and is quickly becoming an indispensable tool in biochemical and genetic research. In the past it has been necessary to manually translate chemical networks into differential equations and then solve them numerically.
Several platforms have been developed that enable biologists to do complex computational simulations of various aspects of cellular signaling and gene regulatory networks. However, these new modeling environments have not been widely utilized in the biological research community. Among the reasons for this lack of acceptance is that the modeling interface is relatively inaccessible for the typical classically-trained geneticist or biochemist. Instead of cartoon representations of signaling pathways in which activation can be represented simply by an arrow connecting two molecular species, users are often asked to write specific differential equations or choose among different modeling approximations. Even for fairly modest biomolecular circuits such a technique would involve explicitly writing dozens (or even hundreds) of differential equations, a job that can be tedious, difficult, and highly error prone, even for an experienced modeler. Thus, there is a strong need for a modeling interface that automatically converts a cartoon- or reaction-based biochemical pathway description into a mathematical representation suitable for the solvers built into various currently existing software packages.
In addition to being more accessible to a broader research community, a tool allowing the automatic generation of mathematical models would facilitate the modeling of complex networks and interactions. For example, in intracellular signal transduction it is not uncommon to find multi-molecular complexes of modifiable proteins. The number of different states, along with the number of equations required to fully describe the dynamics of such a system, increases exponentially with the number of participating molecules or classes of molecules. One typical complex is a scaffold complex involved in MAPK cascades. It is often the case that the dynamics of each state is of interest. A modeler then faces the unpleasant, and potentially error prone task, of writing dozens, if not hundreds, of equations. Therefore, there remains a need for automatic equation generation tools that can significantly ease this task.
Bhalla and Iyengar (Bhalla, U.S., and Iyengar, R., Science 283:381-387 (1999);) have noted the need to systematically study interacting pathways with a standardized scheme, and have described several networks with mass-action kinetics using the Genesis simulator (Bower, J. M., and Beeman. D, The book of Genesis, Springer Verlag, Berlin (1998)). However, Bhalla does not disclose a system for automatically generating a series of differential equations from a user representation of a biological network. Furthermore, this system does not provide the user flexibility to manually intervene and modify differential equations before they are solved. Furthermore, these systems are not robust enough to be utilized for modeling of virtually any biological network such as those involved in developmental systems.